Recombinant Pseudomonas syringae pv. tomato 8-amino-7-oxononanoate synthase (bioF)

What is 8-amino-7-oxononanoate synthase (bioF) and what role does it play in biotin biosynthesis?

8-Amino-7-oxononanoate synthase (AONS) is a pyridoxal 5′-phosphate-dependent enzyme that catalyzes the decarboxylative condensation of L-alanine with pimeloyl-CoA in a stereospecific manner to form 8(S)-amino-7-oxononanoate, coenzyme A, and carbon dioxide. This reaction represents the first committed step in biotin biosynthesis, making it a critical control point in the pathway. The enzyme also demonstrates the ability to catalyze the carboxylation of acetyl-CoA to form malonyl-CoA, which serves as the initial step in fatty acid biosynthesis, indicating its multifunctional nature .

The bioF gene encodes AONS and is found in various bacterial species including Pseudomonas syringae pv. tomato. In the broader context of cellular metabolism, biotin (vitamin H) serves as an essential cofactor for carboxylase enzymes involved in fatty acid synthesis, gluconeogenesis, and amino acid metabolism, making bioF expression critical for bacterial survival and pathogenicity.

What recombineering methods are most effective for genetic manipulation of Pseudomonas syringae?

Recombineering in Pseudomonas syringae is most effectively accomplished using homologous recombination systems based on phage-derived recombination proteins. Research demonstrates that the Pseudomonas RecT homolog is sufficient to promote recombination with single-stranded DNA oligonucleotides, while efficient recombination of double-stranded DNA requires the expression of both RecT and RecE homologs .

For optimal recombineering in P. syringae pv. tomato DC3000, the following methodological approach is recommended:

It's important to note that recombineering systems exhibit narrow species specificity, meaning systems that work well in one species may be non-functional in another. This appears to be the case with Pseudomonas systems, which function robustly in their native species but may not transfer effectively to other organisms .

How can RecET-based systems be optimized for bioF expression in Pseudomonas syringae?

Optimizing RecET-based systems for bioF expression in P. syringae requires careful consideration of several factors:

The RecET system from P. syringae functions through the coordinated action of a 5′-to-3′ exonuclease (RecE) and a single-stranded DNA-annealing and strand invasion protein (RecT). For targeted gene modifications like bioF expression enhancement, the RecT recombinase binds to 3′ ssDNA ends exposed by RecE exonuclease activity, forming a protein-DNA filament that protects the substrate DNA and promotes annealing with homologous genomic sequences .

A methodological approach for optimizing this system includes:

• Promoter selection: Use native Pseudomonas promoters for expressing RecE and RecT to avoid transcriptional incompatibility.

• Induction timing: Induce RecET expression 2-3 hours before introducing the bioF targeting construct.

• Temperature control: Maintain cultures at 28-30°C, the optimal growth temperature for P. syringae.

• Homology arm design: Include 50-100bp homology regions flanking the bioF modification site.

• Counterselection strategy: Incorporate a counterselection marker to screen for successful integration events.

For bioF specifically, designing constructs that target the native chromosomal locus versus episomal expression depends on research goals. Chromosomal integration provides stable expression but at potentially lower levels, while plasmid-based systems offer higher expression but require selection maintenance.

What are the optimal conditions for expressing recombinant bioF in Pseudomonas syringae pv. tomato?

The optimal conditions for expressing recombinant bioF in P. syringae pv. tomato involve careful consideration of growth parameters, expression systems, and physiological conditions:

Expression systems based on native Pseudomonas promoters often provide better results than heterologous systems, as they are better adapted to the host's transcriptional and translational machinery. Integrating the expression construct into the chromosome using the RecET system discussed earlier can provide stable, moderate expression levels suitable for functional studies .

What approaches can be used to study structure-function relationships in bioF?

Understanding structure-function relationships in bioF requires integrating computational, genetic, and biochemical approaches:

Computational Methods:

• Homology modeling: Based on known crystal structures of related PLP-dependent enzymes

• Molecular dynamics simulations: To identify flexible regions and substrate binding dynamics

• Docking studies: To predict substrate binding modes and catalytic interactions

Site-Directed Mutagenesis Approaches:
Target conserved residues in:

• PLP binding pocket

• Substrate binding sites

• Catalytic residues

• Protein-protein interaction interfaces

Methodological Workflow:

• Identify target residues through sequence alignment with characterized AONS enzymes

• Generate mutations using RecET-based recombineering in P. syringae

• Express and purify wild-type and mutant proteins

• Perform comparative analyses:

  • Thermal stability (differential scanning fluorimetry)

  • Substrate binding (isothermal titration calorimetry)

  • Kinetic parameters (as outlined in Q5)

  • PLP binding (absorbance spectroscopy)

Structural Biology Approaches:
For direct structural determination, X-ray crystallography remains the gold standard, requiring:

• High-yield, high-purity protein production

• Crystallization screening (typically 500-1000 conditions)

• Data collection and structure solution

• Model building and refinement

Domain Swapping/Chimeric Proteins:
Creating chimeric proteins between bioF from different Pseudomonas species or even between AONS and related PLP-dependent enzymes can help identify domains responsible for specific functions or substrate preferences.

This integrated approach provides a comprehensive understanding of how specific amino acid residues contribute to enzyme function, substrate specificity, and catalytic efficiency.

How does biofilm formation in Pseudomonas syringae affect recombinant protein expression, including bioF?

Biofilm formation in P. syringae has significant implications for recombinant protein expression, including bioF:

Pseudomonas syringae is known to form biofilms as part of its natural lifecycle, with exopolysaccharides playing a crucial role in biofilm development. Research with P. syringae pv. syringae strain UMAF0158 has demonstrated that cellulose and Psl-like polysaccharide constitute a basic scaffold for biofilm architecture . This physiological state affects protein expression in several ways:

Oxygen and Nutrient Gradients:
Biofilms create microenvironments with varying oxygen and nutrient availability, leading to heterogeneous protein expression throughout the population. This heterogeneity can complicate protein production and purification.

Experimental Approaches to Manage Biofilm Effects:

How should contradictory results in bioF activity assays be analyzed and reconciled?

Contradictory results in bioF activity assays require systematic analysis to identify the source of discrepancies and reconcile conflicting data:

According to research on addressing contradictions in the scientific literature, several categories of factors can explain apparent contradictions :

• Factors internal to the system: Species differences, genetic background, strain variations

• External factors: Experimental conditions, reagent quality, technical variability

• Endogenous/exogenous factors: Interaction with other cellular components or environmental factors

• Known controversies: Recognized disagreements in the field

• Literature contradictions: Incomplete reporting of methods or conditions

Methodological Approach to Reconciliation:

• Categorize the contradiction type:

  • Logical contradiction (p and ¬p simultaneously)

  • Contradiction in literature (opposite reported facts)

  • Contradiction in extracted data (incomplete context)

• Systematically test variables:

  Variable Category	Examples to Test	Methodology
  Enzyme source	Different expression systems, purification methods	Side-by-side comparison
  Assay conditions	pH, temperature, buffer composition	Systematic variation
  Substrate quality	Different lots, sources, purity	LC-MS verification
  Cofactor status	PLP content, binding state	Spectroscopic analysis
  Post-translational modifications	Phosphorylation, oxidation states	Mass spectrometry

• Controlled reconciliation experiments:

  • Use split samples tested under different conditions

  • Employ multiple detection methods for the same reaction

  • Cross-validate between laboratories

  • Perform spike-in recovery experiments

• Statistical analysis:

  • Determine if differences are statistically significant

  • Identify outliers and sources of variability

  • Apply meta-analysis techniques for literature contradictions

When analyzing contradictory results, it's essential to consider context specificity. As seen in research on Pseudomonas, factors like species, dosage, temporal context, and environmental conditions often explain apparent contradictions . For bioF specifically, its dual functionality in biotin and fatty acid biosynthesis pathways may lead to activity differences depending on cellular metabolic state.

What are the emerging techniques for engineering enhanced catalytic efficiency in bioF?

Several cutting-edge approaches are being applied to engineer enzymes with enhanced catalytic properties, which can be applied to bioF:

Directed Evolution Approaches:

• Error-prone PCR: Introducing random mutations throughout the bioF gene

• DNA shuffling: Recombining segments from bioF homologs across Pseudomonas species

• Targeted saturation mutagenesis: Focusing on active site residues or substrate binding pockets

Rational Design Strategies:

• Computational enzyme redesign: Using Rosetta or similar platforms to predict stability-enhancing mutations

• Ancestral sequence reconstruction: Inferring and testing ancestral bioF variants which may have broader substrate ranges

• Loop engineering: Modifying substrate entrance/exit tunnels to improve catalytic rates

Methodological Workflow for bioF Engineering:

• Establish a high-throughput screening system:

  • Colorimetric assay for 8-amino-7-oxononanoate production

  • Growth complementation in bioF-deficient strains

  • Biosensor systems that couple bioF activity to reporter gene expression

• Generate variant libraries:

  • For directed evolution: 103-106 variants

  • For rational design: 10-100 carefully selected variants

  • For semi-rational approaches: focus on hotspots identified through computational analysis

• Iterative improvement cycles:

  • Screen → Characterize → Recombine beneficial mutations → Repeat

• Deep mutational scanning:

  • Comprehensive analysis of all possible single amino acid substitutions

  • Next-generation sequencing to identify enriched variants

  • Machine learning to predict beneficial combination effects

Novel Approaches:

• Incorporation of non-canonical amino acids at key catalytic positions to introduce novel chemical functionalities

• Computational design of protein tunnels to guide substrates more efficiently to the active site

• Dynamic switching modules that alter protein conformation upon substrate binding to enhance catalytic rates

The RecET-based recombineering system identified in Pseudomonas syringae provides an excellent platform for introducing these engineered variants into the native host for in vivo testing and validation.

How can omics-based approaches enhance our understanding of bioF function in Pseudomonas syringae?

Omics-based approaches offer powerful tools for investigating bioF function within the broader context of cellular metabolism and physiology:

Transcriptomics Approaches:

• RNA-Seq analysis: Compare gene expression profiles between wild-type and bioF mutant strains under various conditions

• Transcriptional start site mapping: Identify bioF promoter elements and regulatory features

• Operon structure analysis: Determine if bioF is co-expressed with other biotin biosynthesis genes

Proteomics Strategies:

• Quantitative proteomics: Measure changes in protein abundance in response to bioF manipulation

• Protein-protein interaction studies: Identify bioF interaction partners using techniques like:

  • Affinity purification coupled with mass spectrometry (AP-MS)

  • Bacterial two-hybrid screening

  • In vivo crosslinking followed by immunoprecipitation

• Post-translational modification analysis: Identify regulatory modifications of bioF

Metabolomics Applications:

• Targeted metabolite profiling: Measure levels of biotin, intermediates, and related metabolites

• Flux analysis: Trace isotopically labeled precursors through the biotin pathway

• Metabolome-wide studies: Identify unexpected metabolic connections to bioF activity

Integrative Multi-omics Framework:

Case Study Design for Pseudomonas syringae:

• Construct isogenic strains: wild-type, bioF deletion, complemented mutant, overexpression

• Subject to relevant conditions: minimal vs. rich media, plant extract exposure, biofilm vs. planktonic

• Perform parallel omics analyses

• Integrate data using computational tools like correlation networks or pathway enrichment

• Validate key findings with targeted biochemical assays

This multi-omics approach would be particularly valuable for understanding how bioF expression impacts virulence factors in Pseudomonas syringae pv. tomato, given the connections between bacterial metabolism and pathogenicity observed in plant-pathogen interactions .